

import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[Any]      		=					model.config

							A      :					Optional[int]      		=					DonutSwinConfig(
							    image_size=original_config.input_size     ,				patch_size=4     ,				depths=original_config.encoder_layer     ,				num_heads=[4, 8, 16, 32]     ,				window_size=original_config.window_size     ,				embed_dim=128     ,				)
							A      :					Union[str, Any]      		=					MBartConfig(
							    is_decoder=lowerCamelCase_     ,				is_encoder_decoder=lowerCamelCase_     ,				add_cross_attention=lowerCamelCase_     ,				decoder_layers=original_config.decoder_layer     ,				max_position_embeddings=original_config.max_position_embeddings     ,				vocab_size=len(
							        model.decoder.tokenizer		)     ,				scale_embedding=lowerCamelCase_     ,				add_final_layer_norm=lowerCamelCase_     ,				)

							return encoder_config, decoder_config






def        snake_case__		(    lowerCamelCase_		):
							if "encoder.model" in name:
														A      :					Union[str, Any]      		=					name.replace('''encoder.model'''     ,				'''encoder'''		)
							if "decoder.model" in name:
														A      :					Optional[int]      		=					name.replace('''decoder.model'''     ,				'''decoder'''		)
							if "patch_embed.proj" in name:
														A      :					Union[str, Any]      		=					name.replace('''patch_embed.proj'''     ,				'''embeddings.patch_embeddings.projection'''		)
							if "patch_embed.norm" in name:
														A      :					int      		=					name.replace('''patch_embed.norm'''     ,				'''embeddings.norm'''		)
							if name.startswith('''encoder'''		):
														if "layers" in name:
																					A      :					Tuple      		=					'''encoder.''' + name
														if "attn.proj" in name:
																					A      :					str      		=					name.replace('''attn.proj'''     ,				'''attention.output.dense'''		)
														if "attn" in name and "mask" not in name:
																					A      :					int      		=					name.replace('''attn'''     ,				'''attention.self'''		)
														if "norm1" in name:
																					A      :					int      		=					name.replace('''norm1'''     ,				'''layernorm_before'''		)
														if "norm2" in name:
																					A      :					List[Any]      		=					name.replace('''norm2'''     ,				'''layernorm_after'''		)
														if "mlp.fc1" in name:
																					A      :					Union[str, Any]      		=					name.replace('''mlp.fc1'''     ,				'''intermediate.dense'''		)
														if "mlp.fc2" in name:
																					A      :					List[str]      		=					name.replace('''mlp.fc2'''     ,				'''output.dense'''		)

														if name == "encoder.norm.weight":
																					A      :					int      		=					'''encoder.layernorm.weight'''
														if name == "encoder.norm.bias":
																					A      :					Optional[int]      		=					'''encoder.layernorm.bias'''

							return name






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							for key in orig_state_dict.copy().keys():
														A      :					List[str]      		=					orig_state_dict.pop(lowerCamelCase_		)

														if "qkv" in key:
																					A      :					List[str]      		=					key.split('''.'''		)
																					A      :					Tuple      		=					int(key_split[3]		)
																					A      :					Tuple      		=					int(key_split[5]		)
																					A      :					Union[str, Any]      		=					model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																					if "weight" in key:
																												A      :					str      		=					val[:dim, :]
																												A      :					str      		=					val[dim : dim * 2, :]
																												A      :					List[Any]      		=					val[-dim:, :]
																					else:
																												A      :					Optional[Any]      		=					val[:dim]
																												A      :					Dict      		=					val[dim : dim * 2]
																												A      :					Union[str, Any]      		=					val[-dim:]
														elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
																					# HuggingFace implementation doesn't use attn_mask buffer
																					# and model doesn't use final LayerNorms for the encoder
																					pass
														else:
																					A      :					Optional[Any]      		=					val

							return orig_state_dict






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_=None     ,				lowerCamelCase_=False		):
							# load original model
							A      :					List[Any]      		=					DonutModel.from_pretrained(lowerCamelCase_		).eval()

							# load HuggingFace model
							A		,		A      :					List[Any]      		=					get_configs(lowerCamelCase_		)
							A      :					List[Any]      		=					DonutSwinModel(lowerCamelCase_		)
							A      :					Optional[Any]      		=					MBartForCausalLM(lowerCamelCase_		)
							A      :					Dict      		=					VisionEncoderDecoderModel(encoder=lowerCamelCase_     ,				decoder=lowerCamelCase_		)
							model.eval()

							A      :					Any      		=					original_model.state_dict()
							A      :					Union[str, Any]      		=					convert_state_dict(lowerCamelCase_     ,				lowerCamelCase_		)
							model.load_state_dict(lowerCamelCase_		)

							# verify results on scanned document
							A      :					str      		=					load_dataset('''hf-internal-testing/example-documents'''		)
							A      :					str      		=					dataset['''test'''][0]['''image'''].convert('''RGB'''		)

							A      :					Optional[Any]      		=					XLMRobertaTokenizerFast.from_pretrained(lowerCamelCase_     ,				from_slow=lowerCamelCase_		)
							A      :					Any      		=					DonutImageProcessor(
							    do_align_long_axis=original_model.config.align_long_axis     ,				size=original_model.config.input_size[::-1]		)
							A      :					int      		=					DonutProcessor(lowerCamelCase_     ,				lowerCamelCase_		)
							A      :					Optional[int]      		=					processor(lowerCamelCase_     ,				return_tensors='''pt'''		).pixel_values

							if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
														A      :					Union[str, Any]      		=					'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
														A      :					Union[str, Any]      		=					'''When is the coffee break?'''
														A      :					str      		=					task_prompt.replace('''{user_input}'''     ,				lowerCamelCase_		)
							elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
														A      :					Optional[Any]      		=					'''<s_rvlcdip>'''
							elif model_name in [
							    "naver-clova-ix/donut-base-finetuned-cord-v1",
							    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
							]:
														A      :					int      		=					'''<s_cord>'''
							elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
														A      :					Optional[int]      		=					'''s_cord-v2>'''
							elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
														A      :					str      		=					'''<s_zhtrainticket>'''
							elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
														# use a random prompt
														A      :					Union[str, Any]      		=					'''hello world'''
							else:
														raise ValueError('''Model name not supported'''		)
							A      :					Any      		=					original_model.decoder.tokenizer(lowerCamelCase_     ,				add_special_tokens=lowerCamelCase_     ,				return_tensors='''pt'''		)[
							    '''input_ids'''
							]

							A      :					Optional[Any]      		=					original_model.encoder.model.patch_embed(lowerCamelCase_		)
							A		,		A      :					List[str]      		=					model.encoder.embeddings(lowerCamelCase_		)
							assert torch.allclose(lowerCamelCase_     ,				lowerCamelCase_     ,				atol=1E-3		)

							# verify encoder hidden states
							A      :					int      		=					original_model.encoder(lowerCamelCase_		)
							A      :					Tuple      		=					model.encoder(lowerCamelCase_		).last_hidden_state
							assert torch.allclose(lowerCamelCase_     ,				lowerCamelCase_     ,				atol=1E-2		)

							# verify decoder hidden states
							A      :					List[Any]      		=					original_model(lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		).logits
							A      :					List[str]      		=					model(lowerCamelCase_     ,				decoder_input_ids=lowerCamelCase_		).logits
							assert torch.allclose(lowerCamelCase_     ,				lowerCamelCase_     ,				atol=1E-3		)
							print('''Looks ok!'''		)

							if pytorch_dump_folder_path is not None:
														print(F'Saving model and processor to {pytorch_dump_folder_path}'		)
														model.save_pretrained(lowerCamelCase_		)
														processor.save_pretrained(lowerCamelCase_		)

							if push_to_hub:
														model.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1]     ,				commit_message='''Update model'''		)
														processor.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1]     ,				commit_message='''Update model'''		)


if __name__ == "__main__":
		lowercase			:				str   =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="naver-clova-ix/donut-base-finetuned-docvqa",
		    required=False,
		    type=str,
		    help="Name of the original model you'd like to convert.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path",
		    default=None,
		    required=False,
		    type=str,
		    help="Path to the output PyTorch model directory.",
		)
		parser.add_argument(
		    "--push_to_hub",
		    action="store_true",
		    help="Whether or not to push the converted model and processor to the 🤗 hub.",
		)

		lowercase			:				List[Any]   =     parser.parse_args()
		convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase			:				Tuple   =     logging.get_logger(__name__)







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''pixel_values''']




							def __init__(					self    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = 1 / 2_55    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = True    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       None:
														super().__init__(**__UpperCAmelCase						)
														A      :					Any      		=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
														A      :					Any      		=					get_size_dict(__UpperCAmelCase						)
														A      :					List[Any]      		=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Dict      		=					do_resize
														A      :					Tuple      		=					size
														A      :					Union[str, Any]      		=					resample
														A      :					Dict      		=					do_center_crop
														A      :					int      		=					crop_size
														A      :					Union[str, Any]      		=					do_rescale
														A      :					str      		=					rescale_factor
														A      :					Optional[Any]      		=					do_normalize
														A      :					Dict      		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														A      :					Dict      		=					image_std if image_std is not None else IMAGENET_STANDARD_STD




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = PIL.Image.BICUBIC    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					int      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return resize(
														    __UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						resample=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														A      :					Optional[Any]      		=					get_size_dict(__UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'						)
														return center_crop(__UpperCAmelCase    ,						size=(size['''height'''], size['''width'''])    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       List[str]:
														return rescale(__UpperCAmelCase    ,						scale=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						**__UpperCAmelCase    ,						)		->       np.ndarray:
														return normalize(__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase    ,						data_format=__UpperCAmelCase    ,						**__UpperCAmelCase						)






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = None    ,						__UpperCAmelCase = ChannelDimension.FIRST    ,						**__UpperCAmelCase    ,						)		->       PIL.Image.Image:
														A      :					Optional[int]      		=					do_resize if do_resize is not None else self.do_resize
														A      :					Optional[Any]      		=					resample if resample is not None else self.resample
														A      :					Tuple      		=					do_center_crop if do_center_crop is not None else self.do_center_crop
														A      :					Optional[Any]      		=					do_rescale if do_rescale is not None else self.do_rescale
														A      :					str      		=					rescale_factor if rescale_factor is not None else self.rescale_factor
														A      :					Tuple      		=					do_normalize if do_normalize is not None else self.do_normalize
														A      :					Optional[int]      		=					image_mean if image_mean is not None else self.image_mean
														A      :					Dict      		=					image_std if image_std is not None else self.image_std

														A      :					List[Any]      		=					size if size is not None else self.size
														A      :					List[Any]      		=					get_size_dict(__UpperCAmelCase						)
														A      :					Dict      		=					crop_size if crop_size is not None else self.crop_size
														A      :					Tuple      		=					get_size_dict(__UpperCAmelCase    ,						param_name='''crop_size'''						)

														A      :					Any      		=					make_list_of_images(__UpperCAmelCase						)

														if not valid_images(__UpperCAmelCase						):
																					raise ValueError(
																					    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																					    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

														if do_resize and size is None or resample is None:
																					raise ValueError('''Size and resample must be specified if do_resize is True.'''						)

														if do_center_crop and crop_size is None:
																					raise ValueError('''Crop size must be specified if do_center_crop is True.'''						)

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''Image mean and std must be specified if do_normalize is True.'''						)

														# All transformations expect numpy arrays.
														A      :					List[Any]      		=					[to_numpy_array(__UpperCAmelCase						) for image in images]

														if do_resize:
																					A      :					List[str]      		=					[self.resize(image=__UpperCAmelCase    ,						size=__UpperCAmelCase    ,						resample=__UpperCAmelCase						) for image in images]

														if do_center_crop:
																					A      :					Dict      		=					[self.center_crop(image=__UpperCAmelCase    ,						size=__UpperCAmelCase						) for image in images]

														if do_rescale:
																					A      :					Union[str, Any]      		=					[self.rescale(image=__UpperCAmelCase    ,						scale=__UpperCAmelCase						) for image in images]

														if do_normalize:
																					A      :					Tuple      		=					[self.normalize(image=__UpperCAmelCase    ,						mean=__UpperCAmelCase    ,						std=__UpperCAmelCase						) for image in images]

														A      :					Optional[Any]      		=					[to_channel_dimension_format(__UpperCAmelCase    ,						__UpperCAmelCase						) for image in images]

														A      :					List[Any]      		=					{'''pixel_values''': images}
														return BatchFeature(data=__UpperCAmelCase    ,						tensor_type=__UpperCAmelCase						)






